Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

In this paper, we present a robust and secure watermarking approach using transform domain techniques for tele-health applications. The patient report/identity is embedding into the host medical image for the purpose of authentication, annotation and identification. For better confidentiality, we apply the chaos based encryption algorithm on watermarked image in a less complex manner. Experimental results clearly indicated that the proposed technique is highly robust and sufficient secure for various forms of attacks without any significant distortions between watermarked and cover image. Further, the performance evaluation of our method is found better to existing state-of-the-art watermarking techniques under consideration. Furthermore, quality analysis of the watermarked image is estimated by subjective measure which is beneficial in quality driven healthcare industry.

Multi-layer security of medical data through watermarking and chaotic encryption for tele-health applications

Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

Robustness, imperceptibility and embedding capacity are the preliminary requirements of any watermarking technique. However, research concluded that these requirements are difficult to achieve at same time. In this paper, we review various recent robust and imperceptible watermarking methods in spatial and transform domain. Further, the paper introduces elementary concepts of digital watermarking, characteristics and novel applications of watermark in detail. Furthermore, various analysis and comparison of different notable watermarking techniques are discussed in tabular format. We believe that our survey contribution will helpful for fledgling researchers to develop robust and imperceptible watermarking algorithms for various practical applications.

Survey of robust and imperceptible watermarking

Multiple watermarking technique for securing online social network contents using Back Propagation Neural Network

In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) has been proposed for healthcare applications. For identity authentication purpose, the proposed method uses three watermarks in the form of medical Lump image watermark, the doctor signature/identification code and diagnostic information of the patient as the text watermarks. In order to improve the robustness performance of the image watermark, Back Propagation Neural Network (BPNN) is applied to the extracted image watermark to reduce the noise effects on the watermarked image. The security of the image watermark is also enhanced by using Arnold transform before embedding into the cover. Further, the symptom and signature text watermarks are also encoded by lossless arithmetic compression technique and Hamming error correction code respectively. The compressed and encoded text watermark is then embedded into the cover image. Experimental results are obtained by varying the gain factor, different sizes of text watermarks and the different cover image modalities. The results are provided to illustrate that the proposed method is able to withstand a different of signal processing attacks and has been found to be giving excellent performance for robustness, imperceptibility, capacity and security simultaneously. The robustness performance of the method is also compared with other reported techniques. Finally, the visual quality of the watermarked image is evaluated by the subjective method also. This shows that the visual quality of the watermarked images is acceptable for diagnosis at different gain factors. Therefore the proposed method may find potential application in prevention of patient identity theft in healthcare applications.

A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

Objectives: Increase in traffic density in the world results in more and more congestion, air pollution and accidents. Hence Intelligent Transport System (ITS) has been emerged as a solution to various transport related issues. The aim of this research paper is to conduct systematic analysis on ITS. Methods/Statistical Analysis: ITS is defined as the set of applications which are advance and aim to apply intelligent information and communication technologies in order to provide services for transport and traffic management. ITS have combined various technologies such as Data collection, Communication, Data Mining, Machine Learning, Artificial Intelligence and Database Management. By combining these information technologies ITS have provided various applications such as Traffic control, Fault detection systems, In-vehicle information and navigation systems and driver assistance systems. Findings: We have considered the most relevant published work from 2008 onward relative to our objective from different popular digital libraries. We have summarized this work into issues in ITS and techniques used to solve them. Application/Improvements: It has been found that by combining various new technologies such as agent based computing, cloud computing, VANETS etc. ITS have become very efficient to solving transport related issues in smart cities.

Intelligent Transport System: A Progressive Review

Abstract In this paper, an algorithm for multiple digital watermarking based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) was proposed for healthcare applications such as tele-ophthalmology, tele-medicine, tele-diagnosis, and tele-consultancy services. Multiple watermarks were used in this algorithm to reduce the consequences of medical identity thefts. In the embedding process, the cover medical image was decomposed into third-level DWT. Low-frequency bands (LH2 and LL3) were transformed by DCT, and then SVD was applied to DCT coefficients. Two watermarks in the form of images were also transformed by DCT and then SVD. The singular values of the watermark information were embedded in the singular value of the cover medical image. Watermarks were extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermarks, back-propagation neural network was applied to the extracted watermarks to reduce the effects of different noise applied on the watermarked image. Results were obtained by varying the gain factor and the different cover image modalities. Experimental results were provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks, and has been found to give excellent performance in terms of robustness and imperceptibility. The performance of the method was also compared with other reported techniques. Further, the visual quality of the proposed method was also evaluated by a subjective method.

Multiple Watermarking for Healthcare Applications

Now days advancements in multimedia technology and Information and Communication technologies (ICTs) has raised various security related concerns. Digital image watermarking is one of the new and popular techniques for the protection of multimedia content. This paper proposes an approach for digital image watermarking in which Lifting wavelet transform (LWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) have been used. The host color image is decomposed using LWT and LH3, LL3 sub bands of the third level LWT are transformed by DCT and then SVD. Security of logo (image watermark) is enhanced by using message-digest (MD5) hash algorithm and then DCT-SVD are applied to it before embedding it into LH3 sub band. The robustness of Info (Text watermark) is increased by encoding it using Hamming error correcting code. Error correcting code increases the length of watermark therefore arithmetic coding is applied that provides lossless compression. Experimental results are computed for different color image models (RGB, YIQ, YCbCr) at different gain factors, size of text watermark, different types of cover image and for attacks. The proposed method has good performance for the imperceptibility of watermarked image and robustness of watermarks. The performance of this algorithm can be improved by using various optimization techniques.

Secure and robust color image dual watermarking based on LWT-DCT-SVD

In this paper, an algorithm for digital watermarking based on discrete wavelet transforms (DWTs) and singular value decomposition (SVD) has been proposed. In the embedding process, the host colour image is decomposed into third-level DWT. Low frequency band (LL3) is transformed by SVD. The watermark image is also transformed by SVD. The S vector of watermark information is embedded in the S component of the host image. Watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DWT. Watermark is extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermark, back propagation neural network (BPNN) is applied to the extracted watermark to reduce the effects of different noise applied on the watermarked image. Results are obtained by varying the gain factor and size of the cover and watermark image, experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks and has been found to be giving superior performance for robustness and imperceptibility compared to existing methods suggested by other authors.

Aditi Zear

Papers

A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine

Intelligent Transport System: A Progressive Review

Multiple Watermarking for Healthcare Applications

Secure and robust color image dual watermarking based on LWT-DCT-SVD

Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications